This application claims the priority of Korean Patent Application No. 2003-78873, filed on Nov. 8, 2003, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety by reference.
1. Field of the Invention
The present invention relates to motion estimation of a mobile body, and more particularly, to a motion estimation method and system for a mobile body using compass data.
2. Description of the Related Art
Pose estimation of a mobile body is achieved by estimating a position and an orientation of the mobile body using an absolute sensor or a relative sensor. Pose estimation of a mobile body moving on a 2-dimensional plane as shown in FIG. 1 is achieved by estimating the position (x, y) and the direction θ of the mobile body. In this case, the said absolute sensor can measure the absolute position or orientation of the mobile body instead of its relative motion. A camera, a laser scanner, sonar sensors, a global positioning systems (GPS), or a magnetic compass can be used as an absolute sensor. On the other hand, the said relative sensor can measure a position or an orientation by measuring and integrating a relative increments or decrements value of the motion of the mobile body. A gyro, an accelerometer, and an odometer (an encoder attached to a motor) can be used as the relative sensor.
Some of the characteristics of the absolute sensors are as follows: the camera is sensitive to a lighting status of surrounding environments and has a high possibility of outputting unreliable data; the laser scanner can output reliable data but is very expensive, and if a number of obstacles exist, it is difficult to measure a position or an orientation using the laser scanner; the sonar has low data accuracy; the GPS can be used only outside and has low precision; and the compass has a high possibility of being affected by a disturbance of magnetic field existing indoors. On the other hand, since the relative sensors measure only a variable value, an integration error is inevitably generated by integrating the variable value, and a drift error characteristics of the gyro and the accelerometer cannot be avoided. In an embodiment of the present invention, the disadvantages described above are reduced by sensor fusion using a compass as an absolute sensor and a gyro and an odometer as relative sensors.
A conventional dead-reckoning pose estimation method is classified into a method using only an odometer, a method using a gyro and an odometer, and a method using a compass and an odometer. The method using only the odometer is the simplest one. However, the method using only the odometer cannot cope with a slippage error, a bump collision, or kidnapping. Also, since errors are continuously accumulated in this method, an unbounded error exists.
To solve the problems described above, a method of performing dead-reckoning by using a gyro and an odometer has been developed. However, even though this method can obtain a more accurate result as compared with the method using only the odometer, i.e., the encoder, since both the encoder and the gyro are relative sensors, boundedness cannot be still guaranteed in long-term. A method of stably detecting a moving direction in long-term by introducing the compass, which is the absolute sensor, instead of the gyro also has been developed. However, since this method can easily be affected by a disturbance of magnetic field always existing in a home or office environment, it is difficult to practically use the method.
Recently, a method using a gyro, a compass, and an odometer together has also been suggested. Since this method simultaneously uses a gyro and a compass, mutual-aid between the gyro and the compass is possible. However, since most currently developed methods use the existing Kalman filter, which is a statistics-based sensor fusion method, various limitations originating from inherent demerit of Kalman filter exist. That is, since system noise and measurement noise must have Gaussian white noise characteristics for the application of Kalman filter, it is difficult to deal with various kinds of situations such as a magnetic field, an obstacle, and a slippery floor, which don't show Gaussian white noise characteristics. Besides, exact information of all kinds of models of sensors is necessary, and an assumption that error sources are uncorrelated to each other is also needed. However, practically, a method and system meeting the requirements described above cannot be realized. Also, since the performance of the sensors are important for sensor fusion, expensive sensors are mostly used in this method.